James P. O'Dwyer

Community ecology seeks the mechanisms underlying biodiversity, and simplified models can reveal them. Neutral ecology exemplifies this approach, but it doesn’t distinguish species, limiting its capability for forecasting and prediction. We address this by developing a predictive, minimal model for forest dynamics. We show in a neotropical forest plot that environmental variation couples to the community as a single common effect on all species’ growth rates. Life history acts on that variation, forecasting species’ gains and losses a decade ahead. Finally, immigration, coupled to competition and life history, allows us to predict long-run species abundances. Simple rules thus govern the dynamics of this forest: competition, immigration, and the effect of the environment on growth. Beyond that, species are set apart by their life histories alone.

Species population sizes fluctuate over time, and these temporal dynamics play a key role in governing the maintenance of biodiversity. Although modeling approaches have been developed to characterize fluctuations in species abundances, the data required to parameterize these models from scratch are substantial. Here we introduce a new approach to modeling population fluctuations on decadal timescales by relating community-level dynamics to population-level patterns encoded in plant genomes. Using genomic samples taken at a single time point to generate contemporary effective population size estimates in a temperate montane forest, we accurately predict fluctuations across three censuses. Our approach facilitates the use of genomic demography to parameterize multispecies community models in ecology and shows that population genomic data can provide accurate predictions for ecological dynamics.

The question of what mechanisms maintain tropical biodiversity is a critical frontier in ecology, intensified by the heightened risk of biodiversity loss faced in tropical regions. Ecological theory has shed light on multiple mechanisms that could lead to the high levels of biodiversity in tropical forests. But variation in species abundances over time may be just as important as overall biodiversity, with a more immediate connection to the risk of extirpation and biodiversity loss. Despite the urgency, our understanding of the primary mechanisms driving fluctuations in species abundances has not been clearly established. Here, we introduce a theoretical framework based around life history; the schedule of birth, growth, and mortality over a lifespan, and its systematic variation across species. We develop a mean field model to predict expected fluctuations in abundance for a focal species in a larger community, and we quantify empirical life history variation among 90 tropical forest species in a 50 ha plot in Panama. Putting theory and data together, we show that life history provides a critical piece of this puzzle, allowing us to explain patterns of abundance fluctuations more accurately than previous models incorporating demographic stochasticity without life history variation, and without introducing unobserved couplings between species and their environment. This framework provides a starting point for more general models that incorporate multiple factors in addition to life history variation, and suggests the potential for a fine-grained assessment of extirpation risk based on the impacts of anthropogenic change on demographic rates across life stages.

Designing microbiomes for applications in health, bioengineering, and sustainability is intrinsically linked to a fundamental theoretical understanding of the rules governing microbial community assembly. Microbial ecologists have used a range of mathematical models to understand, predict, and control microbiomes, ranging from mechanistic models, putting microbial populations and their interactions as the focus, to purely statistical approaches, searching for patterns in empirical and experimental data. We review the success and limitations of these modeling approaches when designing novel microbiomes, especially when guided by (inevitably) incomplete experimental data. Although successful at predicting generic patterns of community assembly, mechanistic and phenomenological models tend to fall short of the precision needed to design and implement specific functionality in a microbiome. We argue that to effectively design microbiomes with optimal functions in diverse environments, ecologists should combine data-driven techniques with mechanistic models---a middle, third way for using theory to inform design.

For species that partition resources, the classic expectation is that increasing resource diversity allows for increased species diversity. On the other hand, for neutral species, such as those competing equally for a single resource, diversity reflects a balance between the rate of introduction of novelty (for example by immigration or speciation) and the rate of extinction. Recent models of microbial metabolism have identified scenarios where metabolic trade-offs among species partitioning multiple resources can produce emergent neutral-like dynamics. In this hybrid scenario, one might expect that both resource diversity and immigration will act to boost species diversity. We show, however, that the reverse may be true: when metabolic trade-offs hold and population sizes are sufficiently large, increasing resource diversity can act to reduce species diversity, sometimes drastically. This reversal is explained by a generic transition between neutral- and niche-like dynamics, driven by the diversity of resources. The inverted resource-diversity relationship that results may be a signature of consumer-resource systems with strong metabolic trade-offs.

Life history, the schedule of when and how fast organisms grow, die and reproduce, is a critical axis along which species differ from each other^1,2,3,4. In parallel, competition is a fundamental mechanism that determines the potential for species coexistence^5,6,7,8. Previous models of stochastic competition have demonstrated that large numbers of species can persist over long timescales, even when competing for a single common resource^9,10,11,12, but how life history differences between species increase or decrease the possibility of coexistence and, conversely, whether competition constrains what combinations of life history strategies complement each other remain open questions. Here we show that specific combinations of life history strategy optimize the persistence times of species competing for a single resource before one species overtakes its competitors. This suggests that co-occurring species would tend to have such complementary life history strategies, which we demonstrate using empirical data for perennial plants.

Microbiome engineering offers the potential to lever-age microbial communities to improve outcomes in human health, agriculture, and climate. To translate this potential into reality, it is crucial to reliably predict community composition and function. But a brute force approach to cataloguing community function is hindered by the combinatorial explosion in the number of ways we can combine microbial species. An alternative is to parameterize microbial community outcomes using simplified, mechanistic models, and then extrapolate these models beyond where we have sampled. But these approaches remain data-hungry, as well as requiring an a priori specification of what kinds of mechanism are included and which are omitted. Here, we resolve both issues by introducing a new, mechanism-agnostic approach to predicting microbial community compositions and functions using limited data. The critical step is the discovery of a sparse representation of the community landscape. We then leverage this sparsity to predict community compositions and functions, drawing from techniques in compressive sensing. We validate this approach on in silico community data, generated from a theoretical model. By sampling just ∼ 1% of all possible communities, we accurately predict community compositions out of sample. We then demonstrate the real-world application of our approach by applying it to four experimental datasets, and showing that we can recover interpretable, accurate predictions on composition and community function from highly limited data.

Models of consumer effects on a shared resource environment have helped clarify how the interplay of consumer traits and resource supply impact stable coexistence. Recent models generalize this picture to include the exchange of resources alongside resource competition. These models exemplify the fact that although consumers shape the resource environment, the outcome of consumer interactions is context-dependent: such models can have either stable or unstable equilibria, depending on the resource supply. However, these recent models focus on a simplified version of microbial metabolism where the depletion of resources always leads to consumer growth. Here, we model an arbitrarily large system of consumers governed by Liebig’s law, where species require and deplete multiple resources, but each consumer’s growth rate is only limited by a single one of these resources. Resources that are taken up but not incorporated into new biomass are leaked back into the environment, possibly transformed by intracellular reactions, thereby tying the mismatch between depletion and growth to cross-feeding. For this set of dynamics, we show that feasible equilibria can be either stable or unstable, again depending on the resource environment. We identify special consumption and production networks which protect the community from instability when resources are scarce. Using simulations, we demonstrate that the qualitative stability patterns derived analytically apply to a broader class of network structures and resource inflow profiles, including cases where multiple species coexist on only one externally supplied resource. Our stability criteria bear some resemblance to classic stability results for pairwise interactions, but also demonstrate how environmental context can shape coexistence patterns when resource limitation and exchange are modeled directly.